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在Stan中的一种元分析方法,从多个聚合度-时间数据集中估计人群的药动力学参数:适用于Gevokizumab mPBPK模型
Evangelos Karakitsios1, Aristides Dokoumetzidis1
1Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15784 Athens, Greece.
这项研究引入了一种新方法,通过综合的患者数据来估计人群药动力学 (PopPK) 参数,从而使单克隆抗体和其他治疗药物的更好的药物开发成为可能.
科学领域:
- 药理动力学 药理动力学
- 制药指标 (Pharmacometrics) 是一个指标.
- 药物开发 药物开发
背景情况:
- 估计人群药动力学 (PopPK) 参数和个体间变异性 (IIVs) 对药物开发至关重要.
- 发表的聚合度-时间数据,通常以平均度和标准偏差 (SD) 的形式呈现,经常可用,但难以直接用于PopPK分析.
研究的目的:
- 开发和验证一种方法来估计PopPK参数和IIVs从聚合度-时间数据,特别是从已发表的图表.
- 应用这种方法来估计第二代最小生理学基础的药理动力学 (mPBPK) 模型的PopPK参数,该模型是针对gevokizumab,一种单克隆抗介质素-1β抗体.
主要方法:
- 采用混合效应方法来分析聚合数据,考虑不同剂量组的随机跨组变化 (IGV).
- 分析使用了R软件和贝叶斯工具RStan,并将参数估计的贝叶斯先验纳入分析.
- 该方法专门设计用于处理从已公布的图形数据中平均血度及其SDs与时间的对比.
主要成果:
- 开发的方法成功地使用聚合数据估计了gevokizumab的PopPK参数.
- 该方法证明了使用贝叶斯先验的混合效应模型来估计图形数据中的参数的可行性.
结论:
- 提出的方法是有效的估计PopPK参数从聚合度-时间数据,特别是单克隆抗体使用mPBPK模型.
- 这种方法可以扩展到具有不同的 PK 模型的其他药物,并且对于将多个聚合数据集结合的元分析是有价值的.
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